SoroushVahidi/scidocs huggingface dataset:
Sexuality of male-to-female transsexuals.
(2008)

Blanchard's (J Nerv Ment Dis 177:616-623, 1989) theory of autogynephilia suggests that male-to-female transsexuals can be categorized into different types based on their sexuality. Little previous research has compared the sexuality of male-to-female transsexuals to biological females. The present study examined 15 aspects of sexuality among a non-clinical sample of 234 transsexuals and 127 biological females, using either an online or a paper questionnaire. The results showed that, overall, transsexuals tended to place more importance on partner's physical attractiveness and reported higher scores on Blanchard's Core Autogynephilia Scale than biological females. In addition, transsexuals classified as autogynephilic scored significantly higher on Attraction to Feminine Males, Core Autogynephilia, Autogynephilic Interpersonal Fantasy, Fetishism, Preference for Younger Partners, Interest in Uncommitted Sex, Importance of Partner Physical Attractiveness, and Attraction to Transgender Fiction than other transsexuals and biological females. In accordance with Blanchard's theory, autogynephilia measures were positively correlated to Sexual Attraction to Females among transsexuals. In contrast to Blanchard's theory, however, those transsexuals classified as autogynephilic scored higher on average on Sexual Attraction to Males than those classified as non-autogynephilic, and no transsexuals classified as autogynephilic reported asexuality.
A study of age and gender seen through mobile phone usage patterns in Mexico
(2014)

Mobile phone usage provides a wealth of information, which can be used to better understand the demographic structure of a population. In this paper we focus on the population of Mexican mobile phone users. Our first contribution is an observational study of mobile phone usage according to gender and age groups. We were able to detect significant differences in phone usage among different subgroups of the population. Our second contribution is to provide a novel methodology to predict demographic features (namely age and gender) of unlabeled users by leveraging individual calling patterns, as well as the structure of the communication graph. We provide details of the methodology and show experimental results on a real world dataset that involves millions of users.
Bitcoin Market Return and Volatility Forecasting Using Transaction Network Flow Properties
(2015)

Bit coin, as the foundation for a secure electronic payment system, has drawn broad interests from researchers in recent years. In this paper, we analyze a comprehensive Bit coin transaction dataset and investigate the interrelationship between the flow of Bit coin transactions and its price movement. Using network theory, we examine a few complexity measures of the Bit coin transaction flow networks, and we model the joint dynamic relationship between these complexity measures and Bit coin market variables such as return and volatility. We find that a particular complexity measure of the Bit coin transaction network flow is significantly correlated with the Bit coin market return and volatility. More specifically we document that the residual diversity or freedom of Bit coin network flow scaled by the total system throughput can significantly improve the predictability of Bit coin market return and volatility.
A performance prediction model for the CUDA GPGPU platform
(2009)

The significant growth in computational power of modern Graphics Processing Units (GPUs) coupled with the advent of general purpose programming environments like NVIDIA's CUDA, has seen GPUs emerging as a very popular parallel computing platform. Till recently, there has not been a performance model for GPGPUs. The absence of such a model makes it difficult to definitively assess the suitability of the GPU for solving a particular problem and is a significant impediment to the mainstream adoption of GPUs as a massively parallel (super)computing platform. In this paper we present a performance prediction model for the CUDA GPGPU platform. This model encompasses the various facets of the GPU architecture like scheduling, memory hierarchy, and pipelining among others. We also perform experiments that demonstrate the effects of various memory access strategies. The proposed model can be used to analyze pseudo code for a CUDA kernel to obtain a performance estimate, in a way that is similar to performing asymptotic analysis. We illustrate the usage of our model and its accuracy with three case studies: matrix multiplication, list ranking, and histogram generation.
Unified motion control for dynamic quadrotor maneuvers demonstrated on slung load and rotor failure tasks
(2015)

In recent years impressive results have been presented illustrating the potential of quadrotors to solve challenging tasks. Generally, the derivation of the controllers involve complex analytical manipulation of the dynamics and are very specific to the task at hand. In addition, most approaches construct a trajectory and then design a stabilizing controller in a separate step, whereas a fully optimal solution requires finding both simultaneously. In this paper, a generalized approach is presented using an iterative optimal control algorithm. A series of complex tasks are thus solved using the same algorithm without the need for manual manipulation of the system dynamics, heuristic simplifications, or manual trajectory generation. First, aggressive maneuvers are performed by requiring the quadrotor to pass with a slung load through a window not high enough for the load to pass while hanging straight down. Second, go-to-goal tasks with single and double rotor failure are demonstrated. The adaptability and applicability of this unified approach to such diverse tasks with a nonlinear, underactuated, constrained, and in the case of the slung load, hybrid quadrotor systems is thus shown.
Will 5G See its Blind Side? Evolving 5G for Universal Internet Access
(2016)

Internet has shown itself to be a catalyst for economic growth and social equity but its potency is thwarted by the fact that the Internet is off limits for the vast majority of human beings. Mobile phones—the fastest growing technology in the world that now reaches around 80% of humanity—can enable universal Internet access if it can resolve coverage problems that have historically plagued previous cellular architectures (2G, 3G, and 4G). These conventional architectures have not been able to sustain universal service provisioning since these architectures depend on having enough users per cell for their economic viability and thus are not well suited to rural areas (which are by definition sparsely populated). The new generation of mobile cellular technology (5G), currently in a formative phase and expected to be finalized around 2020, is aimed at orders of magnitude performance enhancement. 5G offers a clean slate to network designers and can be molded into an architecture also amenable to universal Internet provisioning. Keeping in mind the great social benefits of democratizing Internet and connectivity, we believe that the time is ripe for emphasizing universal Internet provisioning as an important goal on the 5G research agenda. In this paper, we investigate the opportunities and challenges in utilizing 5G for global access to the Internet for all (GAIA). We have also identified the major technical issues involved in a 5G-based GAIA solution and have set up a future research agenda by defining open research problems.
Multi-hop Communication in the Uplink for LPWANs
(2017)

Low-Power Wide Area Networks (LPWANs) have arisen as a promising communication technology for supporting Internet of Things (IoT) services due to their low power operation, wide coverage range, low cost and scalability. However, most LPWAN solutions like SIGFOXTMor LoRaWANTMrely on star topology networks, where stations (STAs) transmit directly to the gateway (GW), which often leads to rapid battery depletion in STAs located far from it. In this work, we analyze the impact on LPWANs energy consumption of multi-hop communication in the uplink, allowing STAs to transmit data packets in lower power levels and higher data rates to closer parent STAs, reducing their energy consumption consequently. To that aim, we introduce the DistanceRing Exponential Stations Generator (DRESG) framework, designed to evaluate the performance of the so-called optimal-hop routing model, which establishes optimal routing connections in terms of energy efficiency, aiming to balance the consumption among all the STAs in the network. Results show that enabling such multi-hop connections entails higher network lifetimes, reducing significantly the bottleneck consumption in LPWANs with up to thousands of STAs. These results lead to foresee multi-hop communication in the uplink as a promising routing alternative for extending the lifetime of LPWAN deployments.
Toward automated segmentation of the pathological lung in CT
(2005)

Conventional methods of lung segmentation rely on a large gray value contrast between lung fields and surrounding tissues. These methods fail on scans with lungs that contain dense pathologies, and such scans occur frequently in clinical practice. We propose a segmentation-by-registration scheme in which a scan with normal lungs is elastically registered to a scan containing pathology. When the resulting transformation is applied to a mask of the normal lungs, a segmentation is found for the pathological lungs. As a mask of the normal lungs, a probabilistic segmentation built up out of the segmentations of 15 registered normal scans is used. To refine the segmentation, voxel classification is applied to a certain volume around the borders of the transformed probabilistic mask. Performance of this scheme is compared to that of three other algorithms: a conventional, a user-interactive and a voxel classification method. The algorithms are tested on 10 three-dimensional thin-slice computed tomography volumes containing high-density pathology. The resulting segmentations are evaluated by comparing them to manual segmentations in terms of volumetric overlap and border positioning measures. The conventional and user-interactive methods that start off with thresholding techniques fail to segment the pathologies and are outperformed by both voxel classification and the refined segmentation-by-registration. The refined registration scheme enjoys the additional benefit that it does not require pathological (hand-segmented) training data.
Animal monitoring with unmanned aerial vehicle-aided wireless sensor networks
(2015)

In this paper, we focus on an application of wireless sensor networks (WSNs) with unmanned aerial vehicle (UAV). The aim of the application is to detect the locations of endangered species in large-scale wildlife areas or monitor movement of animals without any attachment devices. We first define the mathematical model of the animal monitoring problem in terms of the value of information (VoI) and rewards. We design a network model including clusters of sensor nodes and a single UAV that acts as a mobile sink and visits the clusters. We propose a path planning approach based on a Markov decision process (MDP) model that maximizes the VoI while reducing message delays. We used real-world movement dataset of zebras. Simulation results show that our approach outperforms greedy and random heuristics as well as the path planning based on the solution of the traveling salesman problem.
HERB: a home exploring robotic butler
(2010)

We describe the architecture, algorithms, and experiments with HERB, an autonomous mobile manipulator that performs useful manipulation tasks in the home. We present new algorithms for searching for objects, learning to navigate in cluttered dynamic indoor scenes, recognizing and registering objects accurately in high clutter using vision, manipulating doors and other constrained objects using caging grasps, grasp planning and execution in clutter, and manipulation on pose and torque constraint manifolds. S.S. Srinivasa ( ) · D. Ferguson · C.J. Helfrich Intel Research Pittsburgh, 4720 Forbes Avenue, Suite 410, Pittsburgh, PA 15213, USA e-mail: siddhartha.srinivasa@intel.com C.J. Helfrich e-mail: casey.j.helfrich@intel.com D. Berenson · A. Collet · R. Diankov · G. Gallagher · G. Hollinger · J. Kuffner · M.V. Weghe The Robotics Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA D. Berenson e-mail: dberenso@ri.cmu.edu A. Collet e-mail: acollet@ri.cmu.edu R. Diankov e-mail: rdiankov@ri.cmu.edu G. Gallagher e-mail: ggallagh@ri.cmu.edu G. Hollinger e-mail: gholling@ri.cmu.edu J. Kuffner e-mail: kuffner@ri.cmu.edu M.V. Weghe e-mail: vandeweg@ri.cmu.edu We also present numerous severe real-world test results from the integration of these algorithms into a single mobile manipulator.
Projected texture stereo
(2010)

Passive stereo vision is widely used as a range sensing technology in robots, but suffers from dropouts: areas of low texture where stereo matching fails. By supplementing a stereo system with a strong texture projector, dropouts can be eliminated or reduced. This paper develops a practical stereo projector system, first by finding good patterns to project in the ideal case, then by analyzing the effects of system blur and phase noise on these patterns, and finally by designing a compact projector that is capable of good performance out to 3m in indoor scenes. The system has been implemented and has excellent depth precision and resolution, especially in the range out to 1.5m.
Real-time 3D object pose estimation and tracking for natural landmark based visual servo
(2008)

A real-time solution for estimating and tracking the 3D pose of a rigid object is presented for image-based visual servo with natural landmarks. The many state-of-the-art technologies that are available for recognizing the 3D pose of an object in a natural setting are not suitable for real-time servo due to their time lags. This paper demonstrates that a real-time solution of 3D pose estimation become feasible by combining a fast tracker such as KLT [7] [8] with a method of determining the 3D coordinates of tracking points on an object at the time of SIFT based tracking point initiation, assuming that a 3D geometric model with SIFT description of an object is known a-priori. Keeping track of tracking points with KLT, removing the tracking point outliers automatically, and reinitiating the tracking points using SIFT once deteriorated, the 3D pose of an object can be estimated and tracked in real-time. This method can be applied to both mono and stereo camera based 3D pose estimation and tracking. The former guarantees higher frame rates with about 1 ms of local pose estimation, while the latter assures of more precise pose results but with about 16 ms of local pose estimation. The experimental investigations have shown the effectiveness of the proposed approach with real-time performance.
Object Recognition from Local Scale-Invariant Features
(1999)

Proc. of the International Conference on Computer Vision, Corfu (Sept. 1999) An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest-neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low-residual least-squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially-occluded images with a computation time of under 2 seconds.
Algorithmic Complexity in Coding Theory and the Minimum Distance Problem
(1997)

We startwithan overviewof algorithmiccomplexity problemsin coding theory We then show that the problemof computing the minimumdiktanceof a binaryIinwr code is NP-hard,and the correspondingdeci~”onproblemis W-complete. Thisconstitutes a proof of the conjecture Bedekamp, McEliece,vanTilborg, dating back to 1978. Extensionsand applicationsof this result to other problemsin codingtheqv are discussed.
Nonparametric belief propagation
(2010)

Continuous quantities are ubiquitous in models of real-world phenomena, but are surprisingly difficult to reason about automatically. Probabilistic graphical models such as Bayesian networks and Markov random fields, and algorithms for approximate inference such as belief propagation (BP), have proven to be powerful tools in a wide range of applications in statistics and artificial intelligence. However, applying these methods to models with continuous variables remains a challenging task. In this work we describe an extension of BP to continuous variable models, generalizing particle filtering, and Gaussian mixture filtering techniques for time series to more complex models. We illustrate the power of the resulting nonparametric BP algorithm via two applications: kinematic tracking of visual motion and distributed localization in sensor networks.
Feature-Enhanced Probabilistic Models for Diffusion Network Inference
(2012)

Cascading processes, such as disease contagion, viral marketing, and information diffusion, are a pervasive phenomenon in many types of networks. The problem of devising intervention strategies to facilitate or inhibit such processes has recently received considerable attention. However, a major challenge is that the underlying network is often unknown. In this paper, we revisit the problem of inferring latent network structure given observations from a diffusion process, such as the spread of trending topics in social media. We define a family of novel probabilistic models that can explain recurrent cascading behavior, and take into account not only the time differences between events but also a richer set of additional features. We show that MAP inference is tractable and can therefore scale to very large real-world networks. Further, we demonstrate the effectiveness of our approach by inferring the underlying network structure of a subset of the popular Twitter following network by analyzing the topics of a large number of messages posted by users over a 10-month period. Experimental results show that our models accurately recover the links of the Twitter network, and significantly improve the performance over previous models based entirely on time.
Map-reduce based parallel support vector machine for risk analysis
(2017)

Now a days people are enjoying the world of data because size and amount of the data has tremendously increased which acts like an invitation to Big data. But some of the classifier techniques like Support Vector Machine (SVM) is not able to handle the huge amount of data due to it's excessive memory requirement and unreasonable complexity in algorithm tough it is one of the most popularly used classifier in machine learning field. Hence a new technique comes into picture which performs parallel algorithm in a efficient way to work data having large scale called as PSVM. In this paper we are going to discuss a PSVM model for risk analysis which is based on map-reduce, and can easily handle a huge amount of data in a distributed manner.
Classificationof Mammographic BreastDensityUsinga CombinedClassifierParadigm Keir Bovis andSameerSingh
(2002)

In thispaperweinvestigateanew approach to theclassificationof mammographic imagesaccording to breasttype. The classificationof breastdensityin this studyis motivatedby its useasprior knowledge in theimageprocessingpipeline.By utilising thisknowledgeat differentstagesincludingenhanceme nt, segmentation and featureextraction, its applicationaims to increasethe sensiti vity of detectingbreastcancer . Our implementeddiscriminationof breastdensityis basedon the underlyingtexture containedwithin the breast tissueapparent on a digital mammogramandrealisedby utilising four approachesto quantifyingthe texture. Following featureextraction,we adopta variationon bootstrapaggregation(’bagging’) to meetthe assumptionsof independencein datarepresentationof theinputdataset,necessaryfor classifiercombination. Multiple classifierscomprisingfeed-forward Artifi cial NeuralNetwork (ANN) aresubseque ntly trainedwith the differentperturbedinput dataspacesusing10-fold cross-validation. Thesetof classifieroutputs,expressedin a probabilistic framework, aresubsequently combinedusingsix differentclassifiercombinationrulesandtheresultscompared.In this studywe examinetwo differentclassificationtasks;a four-classclassificationproblem differentiatingbetweenfatty, partly fatty, denseandextremelydensebreasttypesanda two-classproblems, differentiatingbetweendenseandfatty breasttypes. The datasetusedin this study is the Digital Database of ScreeningMammograms(DDSM) containingMedio-LateralOblique(MLO) views for eachbreastfor 377 patients. For both tasksthe bestcombinationstrategy was found using the productrule giving an average recognition rateon testof 71.4%for thefour-classproblemand96.7%for thetwo-classproblem.
DTC-SVM Scheme for Induction Motors Fedwith a Three-level Inverter
(2008)

Direct Torque Control is a control technique in AC drive systems to obtain high performance torque control. The conventional DTC drive contains a pair of hysteresis comparators. DTC drives utilizing hysteresis comparators suffer from high torque ripple and variable switching frequency. The most common solution to those problems is to use the space vector depends on the reference torque and flux. In this Paper The space vector modulation technique (SVPWM) is applied to 2 level inverter control in the proposed DTC-based induction motor drive system, thereby dramatically reducing the torque ripple. Then the controller based on space vector modulation is designed to be applied in the control of Induction Motor (IM) with a three-level Inverter. This type of Inverter has several advantages over the standard two-level VSI, such as a greater number of levels in the output voltage waveforms, Lower dV/dt, less harmonic distortion in voltage and current waveforms and lower switching frequencies. This paper proposes a general SVPWM algorithm for three-level based on standard two-level SVPWM. The proposed scheme is described clearly and simulation results are reported to demonstrate its effectiveness. The entire control scheme is implemented with Matlab/Simulink. Keywords—Direct torque control, space vector Pulsewidth modulation(SVPWM), neutral point clamped(NPC), two-level inverter.
Data Mining and Knowledge Discovery : Applications , Techniques , Challenges and Process Models in Healthcare
(2013)

Many healthcare leaders find themselves overwhelmed with data, but lack the information they need to make right decisions. Knowledge Discovery in Databases (KDD) can help organizations turn their data into information. Organizations that take advantage of KDD techniques will find that they can lower the healthcare costs while improving healthcare quality by using fast and better clinical decision making. In this paper, a review study is done on existing data mining and knowledge discovery techniques, applications and process models that are applicable to healthcare environments. The challenges for applying data mining techniques in healthcare environment will also be discussed.